feat(infospace): add L3 relation graph with VSM-aware triplets (S2.8)

Implements the L3 relation graph layer — a directed graph of (Subject,
Predicate, Object) triplets annotated with VSM channel codes and feedback
roles. Triplets are authored as markdown files under output/relations/,
parsed into RelationMeta dataclasses, and analysed with networkx.

New modules:
- markitect/infospace/relation_models.py — RelationMeta dataclass +
  RELATION_TYPES controlled vocabulary (15 relation classes → VSM codes)
- markitect/infospace/relation_parser.py — parse_relation_file() and
  parse_relations_directory()

New schema: examples/infospace-with-history/schemas/relation-schema-v1.0.md
  — file naming convention, required sections, controlled vocabulary table

15 seed relation files covering the three core WoN feedback loops:
  - Capital Accumulation loop (positive reinforcement, S1/S3)
  - Market Price Balancing loop (negative feedback, S2/S3)
  - Market Extent mutual dependency (S1/S2)
  Plus structural relations: wages regulation, rent residual, price
  decomposition, invisible hand coordination

CLI: markitect infospace relations [--entity SLUG] [--vsm FILTER]
     [--loops] [--stats]
  - Builds directed graph from parsed files
  - Detects feedback loops via nx.simple_cycles()
  - 6 loops found from 15 seed relations (3 intended + 3 emergent)
  - --stats aggregates by VSM system code (strips parentheticals)

Config: InfospaceConfig gains relations_dir (default output/relations)
infospace.yaml: schemas.relation references relation-schema-v1.0.md

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

markitect/infospace/relation_models.py

@@ -0,0 +1,72 @@
+"""
+Data models for L3 relation triplets.
+
+A relation triplet is the fundamental unit of the relation graph:
+  Subject --[Predicate]--> Object
+
+Each triplet is stored as a markdown file in ``output/relations/``.
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+
+# Controlled relation vocabulary — maps semantic class to VSM channel
+RELATION_TYPES = {
+    "enables": "S1 → S1",
+    "constrains": "S1 ← S1",
+    "regulates": "S3 → S1",
+    "is regulated by": "S1 ← S3",
+    "coordinates": "S2",
+    "produces": "S1",
+    "consumes": "S1",
+    "monitors": "S3*",
+    "audits": "S3*",
+    "adapts to": "S4",
+    "anticipates": "S4",
+    "defines": "S5 → any",
+    "is defined by": "any ← S5",
+    "contradicts": "any",
+    "tensions with": "any",
+}
+
+
+@dataclass
+class RelationMeta:
+    """Structured metadata for a single relation triplet.
+
+    Attributes:
+        slug:          Unique identifier, e.g.
+                       ``division_of_labour--constrains--market_extent``
+        subject:       Human-readable title of the subject entity
+        subject_slug:  Slug of the subject entity (links to L1)
+        predicate:     Human-readable predicate phrase, e.g. "limited by"
+        object:        Human-readable title of the object entity
+        object_slug:   Slug of the object entity (links to L1)
+        relation_type: Semantic class from the controlled vocabulary
+        vsm_channel:   VSM systems involved, e.g. "S1 → S2"
+        evidence:      Source text quote or chapter reference
+        feedback_role: Description of role in a feedback loop (if any)
+        source_path:   Absolute path to the ``.md`` file
+    """
+
+    slug: str
+    subject: str
+    subject_slug: str
+    predicate: str
+    object: str
+    object_slug: str
+    relation_type: str
+    vsm_channel: str
+    evidence: str = ""
+    feedback_role: str = ""
+    source_path: str = ""
+
+    @property
+    def is_feedback_member(self) -> bool:
+        """True if this relation participates in a named feedback loop."""
+        return bool(self.feedback_role.strip())
+
+    def edge(self) -> tuple:
+        """Return a (subject_slug, object_slug, predicate) edge tuple."""
+        return (self.subject_slug, self.object_slug, self.predicate)